DigitalCommons@The Texas Medical Center
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Dysregulated KLF4 Expression Plays a Pivotal Role in the Pathogenesis of Pancreatic Intraductal Papillary Mucinous Neoplasms
No full textCan the Discovery of High-Impact Diagnostics Be Improved by Matching the Sampling Rate of Clinical Diagnostics to the Frequency Domain of Diagnostic Information?
No full textOver the past 30 years, academic and industrial research investigators have developed molecular reporters to visualize cell death in complex biological systems. In parallel, clinical researchers, chemists, biochemists, and molecular biologists have endeavored to translate these molecular tools into clinical imaging agents. Despite these efforts, there are no clinically approved imaging methodologies with which to image cell death consistently and quantitatively. One reason may reside in the intrinsic mismatch between the sampling frequency of translational molecular imaging and the biochemical kinetics that define cell death. Beyond cell death imaging, many active research programs are now attempting to create translational diagnostic pharmaceuticals to image immunological, fibrotic, amyloidotic, and metabolic pathways. Each of these pathways is defined by a unique set of biochemical rate constants, some of which are associated with key predictive pathways. Exhaustively sampling all permutations of pathways and kinetic constants would seem to be an intractable strategy for target identification and validation. Sampling theory, if applied to these pathways, could accelerate the translation of high-impact diagnostics through prioritization of pathways for either AI enhanced diagnostic imaging or AI-enhanced wearable devices. In this perspective, we identify the Nyquist sampling rate as a key criterion for evaluating the optimal application for novel diagnostics. Sampling theory states that to fully characterize a band-limited, stationary, temporal data set, the signal must be sampled at more than twice the rate of the fastest frequency in the signal or, for diagnostics, the discriminatory signal. Through the study of the medical imaging process chain, Nyquist sampling rates of 0.25 day-1 and, more likely, slower than 0.02 day-1 were determined to provide high quality information. By prioritizing low-frequency predictive processes, or state changes, , imaging researchers may improve the hit rate of research programs by appropriately matching the rate of change in diagnostic and predictive information with the limiting sampling rate of medical imaging. Critically, however, high-frequency diagnostic information (and therefore high-frequency biological processes) need not be ignored; these processes are simply better interrogated through continuous monitoring, e.g., by wearable devices combined with machine learning or artificial intelligence
Mature and Migratory Dendritic Cells Promote Immune Infiltration and Response to Anti-Pd-1 Checkpoint Blockade in Metastatic Melanoma
No full textImmune checkpoint inhibitors (ICIs) have revolutionized cancer therapy, yet most patients fail to achieve durable responses. To better understand the tumor microenvironment (TME), we analyze single-cell RNA-seq (~189 K cells) from 36 metastatic melanoma samples, defining 14 cell types, 55 subtypes, and 15 transcriptional hallmarks of malignant cells. Correlations between cell subtype proportions reveal six distinct clusters, with a mature dendritic cell subtype enriched in immunoregulatory molecules (mregDC) linked to naive T and B cells. Importantly, mregDC abundance predicts progression-free survival (PFS) with ICIs and other therapies, especially when combined with the TCF7 + /- CD8 T cell ratio. Analysis of an independent cohort (n = 318) validates mregDC as a predictive biomarker for anti-CTLA-4 plus anti-PD-1 therapies. Further characterization of mregDCs versus conventional dendritic cells (cDC1/cDC2) highlights their unique transcriptional, epigenetic (single-nucleus ATAC-seq data for cDCs from 14 matched samples), and interaction profiles, offering new insights for improving immunotherapy response and guiding future combination treatments
Blood and Neuronal Extracellular Vesicle Mitochondrial Disruptions in Schizophrenia
No full textThe high energy demand of the human brain obligates robust mitochondrial energy metabolism, while mitochondrial dysfunctions have been linked to neuropsychiatric disorders, including schizophrenia spectrum disorders (SSD). However, in vivo assessments that can directly inform brain mitochondrial functioning and its etiopathophysiological path to SSD remain difficult to obtain. We hypothesized that system and brain mitochondrial dysfunctions in SSD may be indexed by elevated cell-free mitochondrial DNA (cf-mtDNA) levels in the blood and in neuronal extracellular vesicles (nEVs). We also explored if these mtDNA marker elevations were associated with brain metabolites as measured by magnetic resonance spectroscopy (MRS). We examined blood cf-mtDNA in 58 SSD patients and 33 healthy controls, followed by assessing nEV mtDNA and metabolite levels using MRS in a subgroup of patients and controls. We found that people with SSD had significantly elevated cf-mtDNA levels in both the blood (p = 0.0002) and neuronal EVs (p = 0.003) compared to controls. These mtDNA abnormalities can be linked back to brain lactate+ levels such that higher blood and nEV mtDNA levels were significantly associated with higher lactate+ levels measured at the anterior cingulate cortex (r = 0.53, 0.53; p = 0.008, 0.03, respectively) in SSD patients. Furthermore, higher developmental stress and trauma were significantly associated with higher cf-mtDNA levels in both the blood and neuronal EVs in SSD patients (r = 0.29, 0.49; p = 0.01, 0.03, respectively). In conclusion, if replicated and fully developed, blood and neuronal EV-based cell-free mtDNA may provide a clinically accessible biomarker to more directly evaluate the mitochondrial hypothesis and the abnormal bioenergetics pathways in schizophrenia
Geographic Disparities in Access to Outpatient Stroke Rehabilitation in Texas
No full textBackground: Outpatient rehabilitation plays a vital role in providing post-discharge care for stroke survivors\u27 optimum recovery. Geographic variability in access to post-stroke rehabilitation care in rural areas is poorly understood.
Methods: This study used Medicare claims from 2016 to 2019 to estimate incidence of stroke discharges home and compared rehabilitation utilization rates after discharge from acute hospitalization in the state of Texas. We also examined spatial accessibility to post-discharge outpatient rehabilitation centers between rural and urban areas. We supplemented claims results with a survey to better understand locations where outpatient rehabilitation clinics provided services to stroke patients.
Results: After discharge from the hospital, patients from rural counties neighboring urban counties had lower adjusted predicted probabilities of using outpatient clinic services compared to urban areas. Patients with primary diagnosis codes of stroke sequelae: adjusted relative rate of 0.84 (CI: 0.76,0.93) with an adjusted rate difference of -0.05 (CI: -0.08, -0.02), cerebral infarction: adjusted relative rate of 0.82 (CI: 0.72,0.91) with adjusted rate difference of -0.04 (CI: -0.06,-0.02), hemorrhagic patients: adjusted relative rate of 0.81 (CI: 0.71,0.91) with an adjusted rate difference of -0.04 (CI: -0.06,-0.01). We did not find discernable differences between rural and urban areas for home health utilization or the combination of outpatient clinic services with home health as a single category. Estimates from a floating-catchment spatial accessibility model scaled from 0 (worst access) to 1 (best access) showed that, compared to urban counties, indices in rural not adjacent to urban counties were -0.16 (CI: -0.23, -0.08) lower and -0.14 (CI: -0.20, -0.08) lower in rural areas in counties adjacent to urban counties.
Conclusions: Compared to urban areas, rural areas have lower spatial access to and utilization of outpatient clinic services in the state of Texas
ADHD in Youth With Major Depressive Disorder in the Texas Youth Depression and Suicide Research Network (TX-YDSRN): Clinical Correlates and Moderators
No full textObjective: Depression is a major public health concern with a 19% lifetime prevalence in youth, often precipitating other concerns, including suicidal behavior, poor school performance, and worsened peer relationships. ADHD is also common among youth and frequently presents alongside major depressive disorder (MDD), with this comorbidity associated with increased impairment. More research is needed to elucidate the clinical characteristics of this comorbidity (MDD + ADHD), especially as it relates to youth with MDD and no ADHD (MDD - ADHD). The present study examined the clinical correlates of MDD + ADHD in youth and the presence of an ADHD diagnosis as a moderator of the relationship between depressive symptoms and suicidality, peer relationships, and school functioning, respectively.
Methods: Our sample included 797 youth with MDD ages 8 to 20 years (Mage = 15.5 years) with and without ADHD.
Results: Youth with MDD + ADHD experienced more severe depressive symptoms, higher levels of suicidality, impulsivity, and irritability, and worse academic performance compared to those with MDD - ADHD. ADHD diagnosis did not moderate the relationships between depression severity and suicidality, peer relationships, or school functioning, respectively, suggesting that having an ADHD diagnosis may not affect these outcomes in depressed youth in this way.
Conclusion: Findings shed light on the impact of ADHD in depressed youth, which may allow for earlier and more tailored intervention efforts aimed at identifying and targeting depression, suicidality, peer relationships, and school functioning
Quantile Regression Based Method for Characterizing Risk-Specific Behavioral Patterns in Relation to Longitudinal Left-Censored Biomarker Data Collected From Heterogeneous Populations
No full textThere are many studies aimed at promoting positive lifestyle behaviors to reduce lifetime risk of cancer and related diseases. However, assessing these modifiable behaviors through statistical modeling is challenging because of the multidimensionality of interrelated measurements that may dramatically differ between at-risk individuals. Taking into account this heterogeneity while considering the multidimensionality of behavior changes is fundamental to tailoring interventions to their needs. Biomarkers that identify high-risk individuals may help validate proximal measures, but the number of validated methods that link biomarkers to multiple behavioral measurements by determining their dynamic relations with disease risks is limited to just a few, since it requires an advanced statistical methodology to address challenges in analyzing biomarker data, including left-censoring due to limits of detection. To address these challenges, we propose a method that constructs a quantile-specific weighted index of multiple behavioral measurements. Under the quantile regression framework, the proposed method renders a multidimensional view of risk-specific behavioral patterns by connecting them with biomarker levels to provide better insights into heterogeneous behavioral profiles among at-risk populations. We evaluate performances of the proposed method through simulations, and illustrate its applications to the Tu Salud ¡Sí Cuenta! data by examining behavior changes among Mexican-American adults
Genome-Wide Association Study for Lung Cancer in 6531 African Americans Reveals New Susceptibility Loci
No full textDespite lung cancer affecting all races and ethnicities, disparities are observed in incidence and mortality rates among different ethnic groups in the United States. Non-Hispanic African Americans had a high incidence rate of lung cancer at 55.8 per 100 000 people, as well as the highest death rate at 37.2 per 100 000 people from 2016 to 2020. While previous genome-wide association studies (GWAS) have identified over 45 susceptibility risk loci that influence lung cancer development, few GWAS have investigated the etiology of lung cancer in African Americans. To address this gap in knowledge, we conducted GWAS of lung cancer focused on studying African Americans, comprising 2267 lung cancer cases and 4264 controls. We identified three loci associated with lung cancer, one with lung adenocarcinoma, and four with lung squamous cell carcinoma in this population at the genomic-wide significance level. Among them, three novel loci were identified near VWF at 12p13.31 for overall lung cancer and GACAT3 at 2p24.3 and LMAN1L at 15q24.1 for lung squamous cell carcinoma. In addition, we confirmed previously reported risk loci with known or new lead variants near CHRNA5 at 15q25.1 and CYP2A6 at 19q13.2 associated with lung cancer and TRIP13 at 5p15.33 and ERC1 at 12p13.33 associated with lung squamous cell carcinoma. Further multi-step functional analyses shed light on biological mechanisms underlying these associations of lung cancer in this population. Our study highlights the importance of ancestry-specific studies for the potential alleviation of lung cancer burden in African Americans
Superior Preclinical Efficacy of Co-Treatment With BRG1/BRM and FLT3 Inhibitor Against AML Cells With FLT3 Mutations
No full textAlthough treatment with standard frontline therapies, including a FLT3 inhibitor (FLT3i) reduces AML burden and achieves clinical remissions, most patients with AML with FLT3 mutation relapse due to therapy-resistant stem/progenitor cells. The core ATPases, BRG1 (SMARCA4) and BRM (SMARCA2) of the canonical (c) BAF (BRG1/BRM-associated factor) complex is a dependency in AML cells, including those harboring FLT3 mutations. We have previously reported that treatment with FHD-286, a BRG1/BRM ATPases inhibitor, induces differentiation and loss of viability of AML stem/progenitor cells. Findings of present studies demonstrate that treatment with FHD-286 induces lethality in AML cells, regardless of sensitivity or resistance to FLT3i. This efficacy is associated with the induction of gene-expression perturbations responsible for growth inhibition, differentiation, as well as a reduced AML-initiating potential of the AML cells. Additionally, co-treatment with FHD-286 and FLT3i exerts superior pre-clinical efficacy against AML cells and patient-derived (PD) xenograft (PDX) models of AML with FLT3 mutations
